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Fung FW, Carpenter JL, Chapman KE, Gallentine W, Giza CC, Goldstein JL, Hahn CD, Loddenkemper T, Matsumoto JH, Press CA, Riviello JJ, Abend NS. Survey of Pediatric ICU EEG Monitoring-Reassessment After a Decade. J Clin Neurophysiol 2024; 41:458-472. [PMID: 36930237 PMCID: PMC10504411 DOI: 10.1097/wnp.0000000000001006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
PURPOSE In 2011, the authors conducted a survey regarding continuous EEG (CEEG) utilization in critically ill children. In the interim decade, the literature has expanded, and guidelines and consensus statements have addressed CEEG utilization. Thus, the authors aimed to characterize current practice related to CEEG utilization in critically ill children. METHODS The authors conducted an online survey of pediatric neurologists from 50 US and 12 Canadian institutions in 2022. RESULTS The authors assessed responses from 48 of 62 (77%) surveyed institutions. Reported CEEG indications were consistent with consensus statement recommendations and included altered mental status after a seizure or status epilepticus, altered mental status of unknown etiology, or altered mental status with an acute primary neurological condition. Since the prior survey, there was a 3- to 4-fold increase in the number of patients undergoing CEEG per month and greater use of written pathways for ICU CEEG. However, variability in resources and workflow persisted, particularly regarding technologist availability, frequency of CEEG screening, communication approaches, and electrographic seizure management approaches. CONCLUSIONS Among the surveyed institutions, which included primarily large academic centers, CEEG use in pediatric intensive care units has increased with some practice standardization, but variability in resources and workflow were persistent.
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Affiliation(s)
- France W Fung
- Departments of Pediatrics and Neurology, Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A
| | - Jessica L Carpenter
- Departments of Pediatrics and Neurology, University of Maryland School of Medicine, Baltimore, Maryland, U.S.A
| | - Kevin E Chapman
- Division of Neurology, Phoenix Children's Hospital and University of Arizona School of Medicine Phoenix, Arizona, U.S.A
| | - William Gallentine
- Division of Neurology, Stanford University and Lucile Packard Children's Hospital, Palo Alto, California, U.S.A
| | - Christopher C Giza
- Division of Neurology, Department of Pediatrics, Mattel Children's Hospital and UCLA Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, U.S.A
| | - Joshua L Goldstein
- Division of Neurology, Children's Memorial Hospital and Northwestern University Feinberg School of Medicine, Chicago, Illinois, U.S.A
| | - Cecil D Hahn
- Division of Neurology, The Hospital for Sick Children and University of Toronto, Toronto, U.S.A
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, U.S.A.; and
| | - Joyce H Matsumoto
- Division of Neurology, Department of Pediatrics, Mattel Children's Hospital and UCLA Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, U.S.A
| | - Craig A Press
- Departments of Pediatrics and Neurology, Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A
| | - James J Riviello
- Division of Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, U.S.A
| | - Nicholas S Abend
- Departments of Pediatrics and Neurology, Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A
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Fung FW, Parikh DS, Donnelly M, Jacobwitz M, Topjian AA, Xiao R, Abend NS. EEG Monitoring in Critically Ill Children: Establishing High-Yield Subgroups. J Clin Neurophysiol 2024; 41:305-311. [PMID: 36893385 PMCID: PMC10492893 DOI: 10.1097/wnp.0000000000000995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Abstract
PURPOSE Continuous EEG monitoring (CEEG) is increasingly used to identify electrographic seizures (ES) in critically ill children, but it is resource intense. We aimed to assess how patient stratification by known ES risk factors would impact CEEG utilization. METHODS This was a prospective observational study of critically ill children with encephalopathy who underwent CEEG. We calculated the average CEEG duration required to identify a patient with ES for the full cohort and subgroups stratified by known ES risk factors. RESULTS ES occurred in 345 of 1,399 patients (25%). For the full cohort, an average of 90 hours of CEEG would be required to identify 90% of patients with ES. If subgroups of patients were stratified by age, clinically evident seizures before CEEG initiation, and early EEG risk factors, then 20 to 1,046 hours of CEEG would be required to identify a patient with ES. Patients with clinically evident seizures before CEEG initiation and EEG risk factors present in the initial hour of CEEG required only 20 (<1 year) or 22 (≥1 year) hours of CEEG to identify a patient with ES. Conversely, patients with no clinically evident seizures before CEEG initiation and no EEG risk factors in the initial hour of CEEG required 405 (<1 year) or 1,046 (≥1 year) hours of CEEG to identify a patient with ES. Patients with clinically evident seizures before CEEG initiation or EEG risk factors in the initial hour of CEEG required 29 to 120 hours of CEEG to identify a patient with ES. CONCLUSIONS Stratifying patients by clinical and EEG risk factors could identify high- and low-yield subgroups for CEEG by considering ES incidence, the duration of CEEG required to identify ES, and subgroup size. This approach may be critical for optimizing CEEG resource allocation.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
| | - Marin Jacobwitz
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphi||a, Pennsylvania, U.S.A
- Department of Anesthesia and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A.; and
| | - Rui Xiao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
- Department of Anesthesia and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A.; and
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A
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Fung FW, Parikh DS, Walsh K, Fitzgerald MP, Massey SL, Topjian AA, Abend NS. Late-Onset Findings During Extended EEG Monitoring Are Rare in Critically Ill Children. J Clin Neurophysiol 2024:00004691-990000000-00131. [PMID: 38687298 DOI: 10.1097/wnp.0000000000001083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024] Open
Abstract
PURPOSE Electrographic seizures (ES) are common in critically ill children undergoing continuous EEG (CEEG) monitoring, and previous studies have aimed to target limited CEEG resources to children at highest risk of ES. However, previous studies have relied on observational data in which the duration of CEEG was clinically determined. Thus, the incidence of late occurring ES is unknown. The authors aimed to assess the incidence of ES for 24 hours after discontinuation of clinically indicated CEEG. METHODS This was a single-center prospective study of nonconsecutive children with acute encephalopathy in the pediatric intensive care unit who underwent 24 hours of extended research EEG after the end of clinical CEEG. The authors assessed whether there were new findings that affected clinical management during the extended research EEG, including new-onset ES. RESULTS Sixty-three subjects underwent extended research EEG. The median duration of the extended research EEG was 24.3 hours (interquartile range 24.0-25.3). Three subjects (5%) had an EEG change during the extended research EEG that resulted in a change in clinical management, including an increase in ES frequency, differential diagnosis of an event, and new interictal epileptiform discharges. No subjects had new-onset ES during the extended research EEG. CONCLUSIONS No subjects experienced new-onset ES during the 24-hour extended research EEG period. This finding supports observational data that patients with late-onset ES are rare and suggests that ES prediction models derived from observational data are likely not substantially underrepresenting the incidence of late-onset ES after discontinuation of clinically indicated CEEG.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Kathleen Walsh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Mark P Fitzgerald
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Shavonne L Massey
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA; and
- Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
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Coleman K, Fung FW, Topjian A, Abend NS, Xiao R. Optimizing EEG monitoring in critically ill children at risk for electroencephalographic seizures. Seizure 2024; 117:244-252. [PMID: 38522169 DOI: 10.1016/j.seizure.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/06/2024] [Accepted: 03/19/2024] [Indexed: 03/26/2024] Open
Abstract
OBJECTIVE Strategies are needed to optimally deploy continuous EEG monitoring (CEEG) for electroencephalographic seizure (ES) identification and management due to resource limitations. We aimed to construct an efficient multi-stage prediction model guiding CEEG utilization to identify ES in critically ill children using clinical and EEG covariates. METHODS The largest prospective single-center cohort of 1399 consecutive children undergoing CEEG was analyzed. A four-stage model was developed and trained to predict whether a subject required additional CEEG at the conclusion of each stage given their risk of ES. Logistic regression, elastic net, random forest, and CatBoost served as candidate methods for each stage and were evaluated using cross validation. An optimal multi-stage model consisting of the top-performing stage-specific models was constructed. RESULTS When evaluated on a test set, the optimal multi-stage model achieved a cumulative specificity of 0.197 and cumulative F1 score of 0.326 while maintaining a high minimum cumulative sensitivity of 0.938. Overall, 11 % of test subjects with ES were removed from the model due to a predicted low risk of ES (falsely negative subjects). CEEG utilization would be reduced by 32 % and 47 % compared to performing 24 and 48 h of CEEG in all test subjects, respectively. We developed a web application called EEGLE (EEG Length Estimator) that enables straightforward implementation of the model. CONCLUSIONS Application of the optimal multi-stage ES prediction model could either reduce CEEG utilization for patients at lower risk of ES or promote CEEG resource reallocation to patients at higher risk for ES.
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Affiliation(s)
- Kyle Coleman
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, United States
| | - France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, United States; Department of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, United States
| | - Alexis Topjian
- Department of Anesthesia and Critical Care, University of Pennsylvania Perelman School of Medicine, United States
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, United States; Department of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, United States; Department of Anesthesia and Critical Care, University of Pennsylvania Perelman School of Medicine, United States; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, United States
| | - Rui Xiao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, United States; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, United States.
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Ney JP, Nuwer MR, Hirsch LJ, Burdelle M, Trice K, Parvizi J. The Cost of After-Hour Electroencephalography. Neurol Clin Pract 2024; 14:e200264. [PMID: 38585440 PMCID: PMC10997216 DOI: 10.1212/cpj.0000000000200264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/21/2023] [Indexed: 04/09/2024]
Abstract
Background and Objectives High costs associated with after-hour electroencephalography (EEG) constitute a barrier for financially constrained hospitals to provide this neurodiagnostic procedure outside regular working hours. Our study aims to deepen our understanding of the cost elements involved in delivering EEG services during after-hours. Methods We accessed publicly available data sets and created a cost model depending on 3 most commonly seen staffing scenarios: (1) technologist on-site, (2) technologist on-call from home, and (3) a hybrid of the two. Results Cost of EEG depends on the volume of testing and the staffing plan. Within the various cost elements, labor cost of EEG technologists is the predominant expenditure, which varies across geographic regions and urban areas. Discussion We provide a model to explain why access to EEGs during after-hours has a substantial expense. This model provides a cost calculator tool (made available as part of this publication in eAppendix 1, links.lww.com/CPJ/A513) to estimate the cost of EEG platform based on site-specific staffing scenarios and annual volume.
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Affiliation(s)
- John P Ney
- School of Medicine (JPN), Boston University, MA; Departments of Neurology (MRN), University of California Los Angeles David Geffen School of Medicine; Department of Neurology (LJH), Yale University School of Medicine, New Haven, CT; Department of Neurology and Neurological Sciences (MB, JP), Stanford University School of Medicine, CA; and Neurodiagnostic Technology Programs (KT), Institute of Health Sciences, Hunt Valley, MD
| | - Marc R Nuwer
- School of Medicine (JPN), Boston University, MA; Departments of Neurology (MRN), University of California Los Angeles David Geffen School of Medicine; Department of Neurology (LJH), Yale University School of Medicine, New Haven, CT; Department of Neurology and Neurological Sciences (MB, JP), Stanford University School of Medicine, CA; and Neurodiagnostic Technology Programs (KT), Institute of Health Sciences, Hunt Valley, MD
| | - Lawrence J Hirsch
- School of Medicine (JPN), Boston University, MA; Departments of Neurology (MRN), University of California Los Angeles David Geffen School of Medicine; Department of Neurology (LJH), Yale University School of Medicine, New Haven, CT; Department of Neurology and Neurological Sciences (MB, JP), Stanford University School of Medicine, CA; and Neurodiagnostic Technology Programs (KT), Institute of Health Sciences, Hunt Valley, MD
| | - Mark Burdelle
- School of Medicine (JPN), Boston University, MA; Departments of Neurology (MRN), University of California Los Angeles David Geffen School of Medicine; Department of Neurology (LJH), Yale University School of Medicine, New Haven, CT; Department of Neurology and Neurological Sciences (MB, JP), Stanford University School of Medicine, CA; and Neurodiagnostic Technology Programs (KT), Institute of Health Sciences, Hunt Valley, MD
| | - Kellee Trice
- School of Medicine (JPN), Boston University, MA; Departments of Neurology (MRN), University of California Los Angeles David Geffen School of Medicine; Department of Neurology (LJH), Yale University School of Medicine, New Haven, CT; Department of Neurology and Neurological Sciences (MB, JP), Stanford University School of Medicine, CA; and Neurodiagnostic Technology Programs (KT), Institute of Health Sciences, Hunt Valley, MD
| | - Josef Parvizi
- School of Medicine (JPN), Boston University, MA; Departments of Neurology (MRN), University of California Los Angeles David Geffen School of Medicine; Department of Neurology (LJH), Yale University School of Medicine, New Haven, CT; Department of Neurology and Neurological Sciences (MB, JP), Stanford University School of Medicine, CA; and Neurodiagnostic Technology Programs (KT), Institute of Health Sciences, Hunt Valley, MD
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Fung FW, Fan J, Parikh DS, Vala L, Donnelly M, Jacobwitz M, Topjian AA, Xiao R, Abend NS. Validation of a Model for Targeted EEG Monitoring Duration in Critically Ill Children. J Clin Neurophysiol 2023; 40:589-599. [PMID: 35512186 PMCID: PMC9582115 DOI: 10.1097/wnp.0000000000000940] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Continuous EEG monitoring (CEEG) to identify electrographic seizures (ES) in critically ill children is resource intense. Targeted strategies could enhance implementation feasibility. We aimed to validate previously published findings regarding the optimal CEEG duration to identify ES in critically ill children. METHODS This was a prospective observational study of 1,399 consecutive critically ill children with encephalopathy. We validated the findings of a multistate survival model generated in a published cohort ( N = 719) in a new validation cohort ( N = 680). The model aimed to determine the CEEG duration at which there was <15%, <10%, <5%, or <2% risk of experiencing ES if CEEG were continued longer. The model included baseline clinical risk factors and emergent EEG risk factors. RESULTS A model aiming to determine the CEEG duration at which a patient had <10% risk of ES if CEEG were continued longer showed similar performance in the generation and validation cohorts. Patients without emergent EEG risk factors would undergo 7 hours of CEEG in both cohorts, whereas patients with emergent EEG risk factors would undergo 44 and 36 hours of CEEG in the generation and validation cohorts, respectively. The <10% risk of ES model would yield a 28% or 64% reduction in CEEG hours compared with guidelines recommending CEEG for 24 or 48 hours, respectively. CONCLUSIONS This model enables implementation of a data-driven strategy that targets CEEG duration based on readily available clinical and EEG variables. This approach could identify most critically ill children experiencing ES while optimizing CEEG use.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Jiaxin Fan
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Lisa Vala
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Marin Jacobwitz
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; and
- Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Rui Xiao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
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Waak M, Laing J, Nagarajan L, Lawn N, Harvey AS. Continuous electroencephalography in the intensive care unit: A critical review and position statement from an Australian and New Zealand perspective. CRIT CARE RESUSC 2023; 25:9-19. [PMID: 37876987 PMCID: PMC10581281 DOI: 10.1016/j.ccrj.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Objectives This article aims to critically review the literature on continuous electroencephalography (cEEG) monitoring in the intensive care unit (ICU) from an Australian and New Zealand perspective and provide recommendations for clinicians. Design and review methods A taskforce of adult and paediatric neurologists, selected by the Epilepsy Society of Australia, reviewed the literature on cEEG for seizure detection in critically ill neonates, children, and adults in the ICU. The literature on routine EEG and cEEG for other indications was not reviewed. Following an evaluation of the evidence and discussion of controversial issues, consensus was reached, and a document that highlighted important clinical, practical, and economic considerations regarding cEEG in Australia and New Zealand was drafted. Results This review represents a summary of the literature and consensus opinion regarding the use of cEEG in the ICU for detection of seizures, highlighting gaps in evidence, practical problems with implementation, funding shortfalls, and areas for future research. Conclusion While cEEG detects electrographic seizures in a significant proportion of at-risk neonates, children, and adults in the ICU, conferring poorer neurological outcomes and guiding treatment in many settings, the health economic benefits of treating such seizures remain to be proven. Presently, cEEG in Australian and New Zealand ICUs is a largely unfunded clinical resource that is subsequently reserved for the highest-impact patient groups. Wider adoption of cEEG requires further research into impact on functional and health economic outcomes, education and training of the neurology and ICU teams involved, and securement of the necessary resources and funding to support the service.
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Affiliation(s)
- Michaela Waak
- Paediatric Critical Care Research Group, Child Health Research Centre, The University of Queensland, Brisbane, Australia
- Paediatric Intensive Care Unit, Queensland Children's Hospital, South Brisbane, Australia
| | - Joshua Laing
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia
- Comprehensive Epilepsy Program, Alfred Health, Melbourne, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Australia
| | - Lakshmi Nagarajan
- Department of Neurology, Perth Children's Hospital, Perth, Australia
- Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia
- Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
| | - Nicholas Lawn
- Western Australian Adult Epilepsy Service, Sir Charles Gardiner Hospital, Perth, Australia
| | - A. Simon Harvey
- Department of Neurology, The Royal Children's Hospital, Melbourne, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
- Neurosciences Research Group, Murdoch Children's Research Institute, Melbourne, Australia
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Sang T, Wang Y, Wu Y, Guan Q, Yang Z. VEEG monitoring and electrographic seizures in 232 pediatric patients in ICU at a tertiary hospital in China. Front Neurol 2022; 13:957465. [PMID: 36504668 PMCID: PMC9726868 DOI: 10.3389/fneur.2022.957465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/07/2022] [Indexed: 11/24/2022] Open
Abstract
Objectives To investigate neonatal electroencephalography (EEG) background activity and electrographic seizures in patients in the pediatric intensive care unit (PICU) who underwent bedside video-electroencephalography (vEEG) monitoring. Methods A total of 232 pediatric patients admitted or transferred to PICU that underwent vEEG monitoring were retrospectively enrolled in this study, and electrographic status epilepticus was observed after vEEG monitoring. Results The median age was 1.56 years [95% confidence interval (CI) = 1.12-2.44]. Electrographic seizures occurred in 88 patients (37.9%), out of which 36 cases (40.9%) had electrographic status epilepticus. Prior epileptic encephalopathy diagnosis [odds ratio (OR) = 6.57, 95% CI = 1.91-22.59, p = 0.003], interictal epileptiform discharges (OR = 46.82, 95%CI = 5.31-412.86, p = 0.0005), slow disorganized EEG background (OR = 11.92, 95%CI = 1.31-108.71, p = 0.028), and burst-suppression EEG background (OR = 23.64, 95%CI = 1.71-327.57, p = 0.018) were the risk factors for electrographic seizures' occurrence. Of the 232 patients, the condition of 179 (77.2%) patients improved and they were discharged, 34 cases (14.7%) were withdrawn, and 18 cases (7.8%) died. The in-hospital death rate was 47.6% (10 in 21 cases) in patients with attenuated/featureless, compared to 0/23 with normal EEG background. Conclusions Electrographic status epilepticus occurs in more than one-third of patients with electrographic seizures. vEEG is an efficient method to determine electrographic seizures in children. Abnormal EEG background activity is associated with both electrographic seizures' occurrence and unfavorable in-hospital outcomes.
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Waak M, Gibbons K, Sparkes L, Harnischfeger J, Gurr S, Schibler A, Slater A, Malone S. Real-time seizure detection in paediatric intensive care patients: the RESET child brain protocol. BMJ Open 2022; 12:e059301. [PMID: 36691237 PMCID: PMC9171209 DOI: 10.1136/bmjopen-2021-059301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 04/19/2022] [Indexed: 01/27/2023] Open
Abstract
INTRODUCTION Approximately 20%-40% of comatose children with risk factors in intensive care have electrographic-only seizures; these go unrecognised due to the absence of continuous electroencephalography (EEG) monitoring (cEEG). Utility of cEEG with high-quality assessment is currently limited due to high-resource requirements. New software analysis tools are available to facilitate bedside cEEG assessment using quantitative EEG (QEEG) trends. The primary aim of this study is to describe accuracy of interpretation of QEEG trends by paediatric intensive care unit (PICU) nurses compared with cEEG assessment by neurologist (standard clinical care) in children at risk of seizures and status epilepticus utilising diagnostic test statistics. The secondary aims are to determine time to seizure detection for QEEG users compared with standard clinical care and describe impact of confounders on accuracy of seizure detection. METHODS AND ANALYSIS This will be a single-centre, prospective observational cohort study evaluating a paediatric QEEG programme utilising the full 19 electrode set. The setting will be a 36-bed quaternary PICU with medical, cardiac and general surgical cases. cEEG studies in PICU patients identified as 'at risk of seizures' will be analysed. Trained bedside clinical nurses will interpret the QEEG. Seizure events will be marked as seizures if >3 QEEG criteria occur. Post-hoc dedicated neurologists, who remain blinded to the QEEG analysis, will interpret the cEEG. Determination of standard test characteristics will assess the primary hypothesis. To calculate 95% (CIs) around the sensitivity and specificity estimates with a CI width of 10%, the sample size needed for sensitivity is 80 patients assuming each EEG will have approximately 9 to 18 1-hour epochs. ETHICS AND DISSEMINATION The study has received approval by the Children's Health Queensland Human Research Ethics Committee (HREC/19/QCHQ/58145). Results will be made available to the funders, critical care survivors and their caregivers, the relevant societies, and other researchers. TRIAL REGISTRATION NUMBER Australian New Zealand Clinical Trials Registry (ANZCTR) 12621001471875.
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Affiliation(s)
- Michaela Waak
- Queensland Children's Hospital Paediatric Intensive Care Unit, South Brisbane, Queensland, Australia
- Centre for Children's Health Research, Brisbane, Queensland, Australia
| | - Kristen Gibbons
- Centre for Children's Health Research, Brisbane, Queensland, Australia
- The University of Queensland, Saint Lucia, Queensland, Australia
| | - Louise Sparkes
- Queensland Children's Hospital Paediatric Intensive Care Unit, South Brisbane, Queensland, Australia
- Centre for Children's Health Research, Brisbane, Queensland, Australia
| | - Jane Harnischfeger
- Queensland Children's Hospital Paediatric Intensive Care Unit, South Brisbane, Queensland, Australia
| | - Sandra Gurr
- Neurosciences, Queensland Children's Hospital, South Brisbane, Queensland, Australia
| | - Andreas Schibler
- St Andrew's War Memorial Hospital, Spring Hill, Queensland, Australia
| | - Anthony Slater
- Queensland Children's Hospital Paediatric Intensive Care Unit, South Brisbane, Queensland, Australia
| | - Stephen Malone
- The University of Queensland, Saint Lucia, Queensland, Australia
- Neurosciences, Queensland Children's Hospital, South Brisbane, Queensland, Australia
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Fung FW, Parikh DS, Massey SL, Fitzgerald MP, Vala L, Donnelly M, Jacobwitz M, Kessler SK, Topjian AA, Abend NS. Periodic and rhythmic patterns in critically ill children: Incidence, interrater agreement, and seizures. Epilepsia 2021; 62:2955-2967. [PMID: 34642942 DOI: 10.1111/epi.17068] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/27/2021] [Accepted: 09/01/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVES We aimed to determine the incidence of periodic and rhythmic patterns (PRP), assess the interrater agreement between electroencephalographers scoring PRP using standardized terminology, and analyze associations between PRP and electrographic seizures (ES) in critically ill children. METHODS This was a prospective observational study of consecutive critically ill children undergoing continuous electroencephalographic monitoring (CEEG). PRP were identified by one electroencephalographer, and then two pediatric electroencephalographers independently scored the first 1-h epoch that contained PRP using standardized terminology. We determined the incidence of PRPs, evaluated interrater agreement between electroencephalographers scoring PRP, and evaluated associations between PRP and ES. RESULTS One thousand three hundred ninety-nine patients underwent CEEG. ES occurred in 345 (25%) subjects. PRP, ES + PRP, and ictal-interictal continuum (IIC) patterns occurred in 142 (10%), 81 (6%), and 93 (7%) subjects, respectively. The most common PRP were generalized periodic discharges (GPD; 43, 30%), lateralized periodic discharges (LPD; 34, 24%), generalized rhythmic delta activity (GRDA; 34, 24%), bilateral independent periodic discharges (BIPD; 14, 10%), and lateralized rhythmic delta activity (LRDA; 11, 8%). ES risk varied by PRP type (p < .01). ES occurrence was associated with GPD (odds ratio [OR] = 6.35, p < .01), LPD (OR = 10.45, p < .01), BIPD (OR = 6.77, p < .01), and LRDA (OR = 6.58, p < .01). Some modifying features increased the risk of ES for each of those PRP. GRDA was not significantly associated with ES (OR = 1.34, p = .44). Each of the IIC patterns was associated with ES (OR = 6.83-8.81, p < .01). ES and PRP occurred within 6 h (before or after) in 45 (56%) subjects. SIGNIFICANCE PRP occurred in 10% of critically ill children who underwent CEEG. The most common patterns were GPD, LPD, GRDA, BIPD, and LRDA. The GPD, LPD, BIPD, LRDA, and IIC patterns were associated with ES. GRDA was not associated with ES.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Shavonne L Massey
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Mark P Fitzgerald
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Lisa Vala
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Marin Jacobwitz
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Sudha K Kessler
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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Abstract
INTRODUCTION Evidence for continuous EEG monitoring in the pediatric intensive care unit (PICU) is increasing. However, 24/7 access to EEG is not routinely available in most centers, and clinical management is often informed by more limited EEG resources. The experience of EEG was reviewed in a tertiary PICU where 24/7 EEG cover is unavailable. METHODS Retrospective EEG and clinical review of 108 PICU patients. Correlations were carried out between EEG and clinical variables including mortality. The role of EEG in clinical decision making was documented. RESULTS One hundred ninety-six EEGs were carried out in 108 PICU patients over 2.5 years (434 hours of recording). After exclusion of 1 outlying patient with epileptic encephalopathy, 136 EEGs (median duration, 65 minutes; range, 20 minutes to 4 hours 40 minutes) were included. Sixty-two patients (57%) were less than 12 months old. Seizures were detected in 18 of 107 patients (17%); 74% of seizures were subclinical; 72% occurred within the first 30 minutes of recording. Adverse EEG findings were associated with high mortality. Antiepileptic drug use was high in the studied population irrespective of EEG seizure detection. Prevalence of epileptiform discharges and EEG seizures diminished with increasing levels of sedation. CONCLUSIONS EEG provides important diagnostic information in a large proportion of PICU patients. In the absence of 24/7 EEG availability, empirical antiepileptic drug utilization is high.
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12
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Machine learning models to predict electroencephalographic seizures in critically ill children. Seizure 2021; 87:61-68. [PMID: 33714840 DOI: 10.1016/j.seizure.2021.03.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/23/2020] [Accepted: 03/02/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE To determine whether machine learning techniques would enhance our ability to incorporate key variables into a parsimonious model with optimized prediction performance for electroencephalographic seizure (ES) prediction in critically ill children. METHODS We analyzed data from a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy who underwent clinically-indicated continuous EEG monitoring (CEEG). We implemented and compared three state-of-the-art machine learning methods for ES prediction: (1) random forest; (2) Least Absolute Shrinkage and Selection Operator (LASSO); and (3) Deep Learning Important FeaTures (DeepLIFT). We developed a ranking algorithm based on the relative importance of each variable derived from the machine learning methods. RESULTS Based on our ranking algorithm, the top five variables for ES prediction were: (1) epileptiform discharges in the initial 30 minutes, (2) clinical seizures prior to CEEG initiation, (3) sex, (4) age dichotomized at 1 year, and (5) epileptic encephalopathy. Compared to the stepwise selection-based approach in logistic regression, the top variables selected by our ranking algorithm were more informative as models utilizing the top variables achieved better prediction performance evaluated by prediction accuracy, AUROC and F1 score. Adding additional variables did not improve and sometimes worsened model performance. CONCLUSION The ranking algorithm was helpful in deriving a parsimonious model for ES prediction with optimal performance. However, application of state-of-the-art machine learning models did not substantially improve model performance compared to prior logistic regression models. Thus, to further improve the ES prediction, we may need to collect more samples and variables that provide additional information.
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13
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Fung FW, Parikh DS, Jacobwitz M, Vala L, Donnelly M, Wang Z, Xiao R, Topjian AA, Abend NS. Validation of a model to predict electroencephalographic seizures in critically ill children. Epilepsia 2020; 61:2754-2762. [PMID: 33063870 DOI: 10.1111/epi.16724] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 09/20/2020] [Accepted: 09/21/2020] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but identification requires extensive resources for continuous electroencephalographic monitoring (CEEG). In a previous study, we developed a clinical prediction rule using three clinical variables (age, acute encephalopathy category, clinically evident seizure[s] prior to CEEG initiation) and two electroencephalographic (EEG) variables (EEG background category and interictal discharges within the first 30 minutes of EEG) to identify patients at high risk for ESs for whom CEEG might be essential. In the current study, we aimed to validate the ES prediction model using an independent cohort. METHODS The prospectively acquired validation cohort consisted of 314 consecutive critically ill children treated in the Pediatric Intensive Care Unit of a quaternary care referral hospital with acute encephalopathy undergoing clinically indicated CEEG. We calculated test characteristics using the previously developed prediction model in the validation cohort. As in the generation cohort study, we selected a 0.10 cutpoint to emphasize sensitivity. RESULTS The incidence of ESs in the validation cohort was 22%. The generation and validation cohorts were alike in most clinical and EEG characteristics. The ES prediction model was well calibrated and well discriminating in the validation cohort. The model had a sensitivity of 90%, specificity of 37%, positive predictive value of 28%, and negative predictive value of 93%. If applied, the model would limit 31% of patients from undergoing CEEG while failing to identify 10% of patients with ESs. The model had similar performance characteristics in the generation and validation cohorts. SIGNIFICANCE A model employing five readily available clinical and EEG variables performed well when validated in a new consecutive cohort. Implementation would substantially reduce CEEG utilization, although some patients with ESs would not be identified. This model may serve a critical role in targeting limited CEEG resources to critically ill children at highest risk for ESs.
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Affiliation(s)
- France W Fung
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Departments Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Darshana S Parikh
- Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Marin Jacobwitz
- Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lisa Vala
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Zi Wang
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Rui Xiao
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nicholas S Abend
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Departments Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Department of Anesthesia and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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14
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Naim MY, Putt M, Abend NS, Mastropietro CW, Frank DU, Chen JM, Fuller S, Gangemi JJ, Gaynor JW, Heinan K, Licht DJ, Mascio CE, Massey S, Roeser ME, Smith CJ, Kimmel SE. Development and Validation of a Seizure Prediction Model in Neonates After Cardiac Surgery. Ann Thorac Surg 2020; 111:2041-2048. [PMID: 32738224 DOI: 10.1016/j.athoracsur.2020.05.157] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 05/22/2020] [Accepted: 05/26/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Electroencephalographic seizures (ESs) after neonatal cardiac surgery are often subclinical and have been associated with poor outcomes. An accurate ES prediction model could allow targeted continuous electroencephalographic monitoring (CEEG) for high-risk neonates. METHODS ES prediction models were developed and validated in a multicenter prospective cohort where all postoperative neonates who underwent cardiopulmonary bypass (CPB) also underwent CEEG. RESULTS ESs occurred in 7.4% of neonates (78 of 1053). Model predictors included gestational age, head circumference, single-ventricle defect, deep hypothermic circulatory arrest duration, cardiac arrest, nitric oxide, extracorporeal membrane oxygenation, and delayed sternal closure. The model performed well in the derivation cohort (c-statistic, 0.77; Hosmer-Lemeshow, P = .56), with a net benefit (NB) over monitoring all and none over a threshold probability of 2% in decision curve analysis (DCA). The model had good calibration in the validation cohort (Hosmer-Lemeshow, P = .60); however, discrimination was poor (c-statistic, 0.61), and in DCA there was no NB of the prediction model between the threshold probabilities of 8% and 18%. By using a cut point that emphasized negative predictive value in the derivation cohort, 32% (236 of 737) of neonates would not undergo CEEG, including 3.5% (2 of 58) of neonates with ESs (negative predictive value, 99%; sensitivity, 97%). CONCLUSIONS In this large prospective cohort, a prediction model of ESs in neonates after CPB had good performance in the derivation cohort, with an NB in DCA. However, performance in the validation cohort was weak, with poor discrimination, poor calibration, and no NB in DCA. These findings support CEEG of all neonates after CPB.
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Affiliation(s)
- Maryam Y Naim
- Division of Cardiac Critical Care Medicine, Department of Anesthesiology, Critical Care Medicine, and Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Anesthesiology and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Mary Putt
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Nicholas S Abend
- Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christopher W Mastropietro
- Division of Critical Care, Department of Pediatrics, Riley Hospital for Children at Indiana University Health, Indiana University School of Medicine, Indianapolis, Indiana
| | - Deborah U Frank
- Division of Critical Care, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
| | - Jonathan M Chen
- Division of Cardiothoracic Surgery, Department of Surgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Stephanie Fuller
- Division of Cardiothoracic Surgery, Department of Surgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - James J Gangemi
- Division of Cardiothoracic Surgery, Department of Surgery, University of Virginia, Charlottesville, Virginia
| | - J William Gaynor
- Division of Cardiothoracic Surgery, Department of Surgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Kristin Heinan
- Division of Neurology, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
| | - Daniel J Licht
- Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christopher E Mascio
- Division of Cardiothoracic Surgery, Department of Surgery, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Shavonne Massey
- Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mark E Roeser
- Division of Cardiothoracic Surgery, Department of Surgery, University of Virginia, Charlottesville, Virginia
| | - Clyde J Smith
- Division of Critical Care, Department of Pediatrics, University of Virginia, Charlottesville, Virginia
| | - Stephen E Kimmel
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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15
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Fung FW, Fan J, Vala L, Jacobwitz M, Parikh DS, Donnelly M, Topjian AA, Xiao R, Abend NS. EEG monitoring duration to identify electroencephalographic seizures in critically ill children. Neurology 2020; 95:e1599-e1608. [PMID: 32690798 DOI: 10.1212/wnl.0000000000010421] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Accepted: 04/10/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVES To determine the optimal duration of continuous EEG monitoring (CEEG) for electrographic seizure (ES) identification in critically ill children. METHODS We performed a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy. We evaluated baseline clinical risk factors (age and prior clinically evident seizures) and emergent CEEG risk factors (epileptiform discharges and ictal-interictal continuum patterns) using a multistate survival model. For each subgroup, we determined the CEEG duration for which the risk of ES was <5% and <2%. RESULTS ES occurred in 184 children (26%). Patients achieved <5% risk of ES after (1) 6 hours if ≥1 year without prior seizures or EEG risk factors; (2) 1 day if <1 year without prior seizures or EEG risks; (3) 1 day if ≥1 year with either prior seizures or EEG risks; (4) 2 days if ≥1 year with prior seizures and EEG risks; (5) 2 days if <1 year without prior seizures but with EEG risks; and (6) 2.5 days if <1 year with prior seizures regardless of the presence of EEG risks. Patients achieved <2% risk of ES at the same durations except patients without prior seizures or EEG risk factors would require longer CEEG (1.5 days if <1 year of age, 1 day if ≥1 year of age). CONCLUSIONS A model derived from 2 baseline clinical risk factors and emergent EEG risk factors would allow clinicians to implement personalized strategies that optimally target limited CEEG resources. This would enable more widespread use of CEEG-guided management as a potential neuroprotective strategy. CLINICALTRIALSGOV IDENTIFIER NCT03419260.
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Affiliation(s)
- France W Fung
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
| | - Jiaxin Fan
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Lisa Vala
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Marin Jacobwitz
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Darshana S Parikh
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Maureen Donnelly
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Alexis A Topjian
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Rui Xiao
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Nicholas S Abend
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
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16
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Griffith JL, Tomko ST, Guerriero RM. Continuous Electroencephalography Monitoring in Critically Ill Infants and Children. Pediatr Neurol 2020; 108:40-46. [PMID: 32446643 DOI: 10.1016/j.pediatrneurol.2020.04.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 12/15/2022]
Abstract
Continuous video electroencephalography (CEEG) monitoring of critically ill infants and children has expanded rapidly in recent years. Indications for CEEG include evaluation of patients with altered mental status, characterization of paroxysmal events, and detection of electrographic seizures, including monitoring of patients with limited neurological examination or conditions that put them at high risk for electrographic seizures (e.g., cardiac arrest or extracorporeal membrane oxygenation cannulation). Depending on the inclusion criteria and clinical characteristics of the population studied, the percentage of pediatric patients with electrographic seizures varies from 7% to 46% and with electrographic status epilepticus from 1% to 23%. There is also evidence that epileptiform and background CEEG patterns may provide important information about prognosis in certain clinical populations. Quantitative EEG techniques are emerging as a tool to enhance the value of CEEG to provide real-time bedside data for management and prognosis. Continued research is needed to understand the clinical value of seizure detection and identification of other CEEG patterns on the outcomes of critically ill infants and children.
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Affiliation(s)
- Jennifer L Griffith
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri.
| | - Stuart T Tomko
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Réjean M Guerriero
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
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17
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Fung FW, Jacobwitz M, Parikh DS, Vala L, Donnelly M, Fan J, Xiao R, Topjian AA, Abend NS. Development of a model to predict electroencephalographic seizures in critically ill children. Epilepsia 2020; 61:498-508. [PMID: 32077099 DOI: 10.1111/epi.16448] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/23/2020] [Accepted: 01/23/2020] [Indexed: 01/10/2023]
Abstract
OBJECTIVE Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but ES identification with continuous electroencephalography (EEG) monitoring (CEEG) is resource-intense. We aimed to develop an ES prediction model that would enable clinicians to stratify patients by ES risk and optimally target limited CEEG resources. We aimed to determine whether incorporating data from a screening EEG yielded better performance characteristics than models using clinical variables alone. METHODS We performed a prospective observational study of 719 consecutive critically ill children with acute encephalopathy undergoing CEEG in the pediatric intensive care unit of a quaternary care institution between April 2017 and February 2019. We identified clinical and EEG risk factors for ES. We evaluated model performance with area under the receiver-operating characteristic (ROC) curve (AUC), validated the optimal model with the highest AUC using a fivefold cross-validation, and calculated test characteristics emphasizing high sensitivity. We applied the optimal operating slope strategy to identify the optimal cutoff to define whether a patient should undergo CEEG. RESULTS The incidence of ES was 26%. Variables associated with increased ES risk included age, acute encephalopathy category, clinical seizures prior to CEEG initiation, EEG background, and epileptiform discharges. Combining clinical and EEG variables yielded better model performance (AUC 0.80) than clinical variables alone (AUC 0.69; P < .01). At a 0.10 cutoff selected to emphasize sensitivity, the optimal model had a sensitivity of 92%, specificity of 37%, positive predictive value of 34%, and negative predictive value of 93%. If applied, the model would limit 29% of patients from undergoing CEEG while failing to identify 8% of patients with ES. SIGNIFICANCE A model employing readily available clinical and EEG variables could target limited CEEG resources to critically ill children at highest risk for ES, making CEEG-guided management a more viable neuroprotective strategy.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marin Jacobwitz
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lisa Vala
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jiaxin Fan
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rui Xiao
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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Worden LT, Chinappen DM, Stoyell SM, Gold J, Paixao L, Krishnamoorthy K, Kramer MA, Westover MB, Chu CJ. The probability of seizures during continuous EEG monitoring in high-risk neonates. Epilepsia 2019; 60:2508-2518. [PMID: 31745988 DOI: 10.1111/epi.16387] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 10/17/2019] [Accepted: 10/21/2019] [Indexed: 12/20/2022]
Abstract
OBJECTIVE We evaluated the impact of monitoring indication, early electroencephalography (EEG), and clinical features on seizure risk in all neonates undergoing continuous EEG (cEEG) monitoring following a standardized monitoring protocol. METHODS All cEEGs from unique neonates 34-48 weeks postmenstrual age monitored from 1/2011-10/2017 (n = 291) were included. We evaluated the impact of cEEG monitoring indication (acute neonatal encephalopathy [ANE], suspicious clinical events [SCEs], or other high-risk conditions [OHRs]), age, medication status, and early EEG abnormalities (including the presence of epileptiform discharges and abnormal background continuity, amplitude, asymmetry, asynchrony, excessive sharp transients, and burst suppression) on time to first seizure and overall seizure risk using Kaplan-Meier survival curves and multivariable Cox proportional hazards models. RESULTS Seizures occurred in 28% of high-risk neonates. Discontinuation of monitoring after 24 hours of seizure-freedom would have missed 8.5% of neonates with seizures. Overall seizure risk was lower in neonates monitored for ANE compared to OHR (P = .004) and trended lower compared to SCE (P = .097). The time course of seizure presentation varied by group, where the probability of future seizure was less than 1% after 17 hours of seizure-free monitoring in the SCE group, but required 42 hours in the OHR group, and 73 hours in the ANE group. The presence of early epileptiform discharges increased seizure risk in each group (ANE: adjusted hazard ratio [aHR] 4.32, 95% confidence interval [CI] 1.23-15.13, P = .022; SCE: aHR 10.95, 95% CI 4.77-25.14, P < 1e-07; OHR: aHR 56.90, 95% CI 10.32-313.72, P < 1e-05). SIGNIFICANCE Neonates who undergo cEEG are at high risk for seizures, and risk varies by monitoring indication and early EEG findings. Seizures are captured in nearly all neonates undergoing monitoring for SCE within 24 hours of cEEG monitoring. Neonates monitored for OHR and ANE can present with delayed seizures and require longer durations of monitoring. Early epileptiform discharges are the best early EEG feature to predict seizure risk.
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Affiliation(s)
- Lila T Worden
- Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | | | - Jacquelyn Gold
- Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Luis Paixao
- Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Mark A Kramer
- Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Michael B Westover
- Neurology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Catherine J Chu
- Neurology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
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19
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Hill CE, Blank LJ, Thibault D, Davis KA, Dahodwala N, Litt B, Willis AW. Continuous EEG is associated with favorable hospitalization outcomes for critically ill patients. Neurology 2018; 92:e9-e18. [PMID: 30504428 DOI: 10.1212/wnl.0000000000006689] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 08/29/2018] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE To characterize continuous EEG (cEEG) use patterns in the critically ill and to determine the association with hospitalization outcomes for specific diagnoses. METHODS We performed a retrospective cross-sectional study with National Inpatient Sample data from 2004 to 2013. We sampled hospitalized adult patients who received intensive care and then compared patients who underwent cEEG to those who did not. We considered diagnostic subgroups of seizure/status epilepticus, subarachnoid or intracerebral hemorrhage, and altered consciousness. Outcomes were in-hospital mortality, hospitalization cost, and length of stay. RESULTS In total, 7,102,399 critically ill patients were identified, of whom 22,728 received cEEG. From 2004 to 2013, the proportion of patients who received cEEG increased from 0.06% (95% confidence interval [CI] 0.03%-0.09%) to 0.80% (95% CI 0.62%-0.98%). While the cEEG cohort appeared more ill, cEEG use was associated with reduced in-hospital mortality after adjustment for patient and hospital characteristics (odds ratio [OR] 0.83, 95% CI 0.75-0.93, p < 0.001). This finding held for the diagnoses of subarachnoid or intracerebral hemorrhage and for altered consciousness but not for the seizure/status epilepticus subgroup. Cost and length of hospitalization were increased for the cEEG cohort (OR 1.17 and OR 1.11, respectively, p < 0.001). CONCLUSIONS There was a >10-fold increase in cEEG use from 2004 to 2013. However, this procedure may still be underused; cEEG was associated with lower in-hospital mortality but used for only 0.3% of the critically ill population. While administrative claims analysis supports the utility of cEEG for critically ill patients, our findings suggest variable benefit by diagnosis, and investigation with greater clinical detail is warranted.
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Affiliation(s)
- Chloe E Hill
- From the Department of Neurology (C.E.H., L.J.B., D.T., K.A.D., N.D., B.L., A.W.W.), Leonard Davis Institute of Health Economics (C.E.H., N.D., A.W.W.), Translational Center of Excellence for Neurology Outcomes Research, Department of Neurology (D.T., A.W.W.), and Department of Biostatistics, Epidemiology and Informatics (A.W.W.), University of Pennsylvania, Philadelphia.
| | - Leah J Blank
- From the Department of Neurology (C.E.H., L.J.B., D.T., K.A.D., N.D., B.L., A.W.W.), Leonard Davis Institute of Health Economics (C.E.H., N.D., A.W.W.), Translational Center of Excellence for Neurology Outcomes Research, Department of Neurology (D.T., A.W.W.), and Department of Biostatistics, Epidemiology and Informatics (A.W.W.), University of Pennsylvania, Philadelphia
| | - Dylan Thibault
- From the Department of Neurology (C.E.H., L.J.B., D.T., K.A.D., N.D., B.L., A.W.W.), Leonard Davis Institute of Health Economics (C.E.H., N.D., A.W.W.), Translational Center of Excellence for Neurology Outcomes Research, Department of Neurology (D.T., A.W.W.), and Department of Biostatistics, Epidemiology and Informatics (A.W.W.), University of Pennsylvania, Philadelphia
| | - Kathryn A Davis
- From the Department of Neurology (C.E.H., L.J.B., D.T., K.A.D., N.D., B.L., A.W.W.), Leonard Davis Institute of Health Economics (C.E.H., N.D., A.W.W.), Translational Center of Excellence for Neurology Outcomes Research, Department of Neurology (D.T., A.W.W.), and Department of Biostatistics, Epidemiology and Informatics (A.W.W.), University of Pennsylvania, Philadelphia
| | - Nabila Dahodwala
- From the Department of Neurology (C.E.H., L.J.B., D.T., K.A.D., N.D., B.L., A.W.W.), Leonard Davis Institute of Health Economics (C.E.H., N.D., A.W.W.), Translational Center of Excellence for Neurology Outcomes Research, Department of Neurology (D.T., A.W.W.), and Department of Biostatistics, Epidemiology and Informatics (A.W.W.), University of Pennsylvania, Philadelphia
| | - Brian Litt
- From the Department of Neurology (C.E.H., L.J.B., D.T., K.A.D., N.D., B.L., A.W.W.), Leonard Davis Institute of Health Economics (C.E.H., N.D., A.W.W.), Translational Center of Excellence for Neurology Outcomes Research, Department of Neurology (D.T., A.W.W.), and Department of Biostatistics, Epidemiology and Informatics (A.W.W.), University of Pennsylvania, Philadelphia
| | - Allison W Willis
- From the Department of Neurology (C.E.H., L.J.B., D.T., K.A.D., N.D., B.L., A.W.W.), Leonard Davis Institute of Health Economics (C.E.H., N.D., A.W.W.), Translational Center of Excellence for Neurology Outcomes Research, Department of Neurology (D.T., A.W.W.), and Department of Biostatistics, Epidemiology and Informatics (A.W.W.), University of Pennsylvania, Philadelphia
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20
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Naim MY, Rossano JW. Decreasing neurologic injury in children after hypoxic injury: Is transcutaneous doppler the way to go? Resuscitation 2018. [PMID: 29524478 DOI: 10.1016/j.resuscitation.2018.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Maryam Y Naim
- The Cardiac Center, The Children's Hospital of Philadelphia, Departments of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States; Anesthesiology and Critical Care Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
| | - Joseph W Rossano
- The Cardiac Center, The Children's Hospital of Philadelphia, Departments of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States.
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21
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Baldassano SN, Brinkmann BH, Ung H, Blevins T, Conrad EC, Leyde K, Cook MJ, Khambhati AN, Wagenaar JB, Worrell GA, Litt B. Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings. Brain 2017; 140:1680-1691. [PMID: 28459961 DOI: 10.1093/brain/awx098] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 02/26/2017] [Indexed: 11/14/2022] Open
Abstract
There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care.
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Affiliation(s)
- Steven N Baldassano
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Benjamin H Brinkmann
- Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA.,Department of Neurology, Mayo Clinic and Mayo Foundation, Rochester, MN 55905, USA
| | - Hoameng Ung
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler Blevins
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erin C Conrad
- Department of Neurology, University of Pennsylvania, PA, USA
| | | | - Mark J Cook
- St. Vincent's Hospital, Melbourne, VIC, Australia.,Department of Medicine, University of Melbourne, Melbourne, VIC, Australia
| | - Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joost B Wagenaar
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Neurology, University of Pennsylvania, PA, USA
| | - Gregory A Worrell
- Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA.,Department of Neurology, Mayo Clinic and Mayo Foundation, Rochester, MN 55905, USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Neurology, University of Pennsylvania, PA, USA
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22
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Abstract
OBJECTIVE We aimed to determine the prevalence and risk factors for electrographic seizures in neonates and children requiring extracorporeal membrane oxygenation support. DESIGN Prospective quality improvement project. SETTING Quaternary care pediatric institution. PATIENTS Consistent with American Clinical Neurophysiology Society electroencephalographic monitoring recommendations, neonates and children requiring extracorporeal membrane oxygenation support underwent clinically indicated electroencephalographic monitoring. INTERVENTIONS We performed a 2-year quality improvement study from July 2013 to June 2015 evaluating electrographic seizure prevalence and risk factors. MAIN RESULTS Ninety-nine of 112 patients (88%) requiring extracorporeal membrane oxygenation support underwent electroencephalographic monitoring. Electrographic seizures occurred in 18 patients (18%), of whom 11 patients (61%) had electrographic status epilepticus and 15 patients (83%) had exclusively electrographic-only seizures. Electrographic seizures were more common in patients with low cardiac output syndrome (p = 0.03). Patients with electrographic seizures were more likely to die prior to discharge (72% vs 30%; p = 0.01) and have unfavorable outcomes (54% vs 17%; p = 0.004) than those without electrographic seizures. CONCLUSIONS Electrographic seizures occurred in 18% of neonates and children requiring extracorporeal membrane oxygenation support, often constituted electrographic status epilepticus, and were often electrographic-only thereby requiring electroencephalographic monitoring for identification. Low cardiac output syndrome was associated with an increased risk for electrographic seizures. Electrographic seizures were associated with higher mortality and unfavorable outcomes. Further investigation is needed to determine whether electrographic seizures identification and management improves outcomes.
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23
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Could EEG Monitoring in Critically Ill Children Be a Cost-effective Neuroprotective Strategy? J Clin Neurophysiol 2016; 32:486-94. [PMID: 26057408 DOI: 10.1097/wnp.0000000000000198] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
PURPOSE Electrographic status epilepticus (ESE) in critically ill children is associated with unfavorable functional outcomes, but identifying candidates for ESE management requires resource-intense EEG monitoring. A cost-effectiveness analysis was performed to estimate how much ESE identification and management would need to improve patient outcomes to make EEG monitoring strategies a good value. METHODS A decision tree was created to examine the relationships among variables important to deciding whether to perform EEG monitoring. Variable costs were estimated from their component parts, outcomes were estimated in quality-adjusted life-years, and incremental cost-effectiveness ratios were calculated to compare the relative values using four alternative EEG monitoring strategies that varied by monitoring duration. RESULTS Forty-eight hours of EEG monitoring would be worth its cost if ESE identification and management improved patient outcomes by ≥7%. If ESE identification and management improved patient outcomes by 3% to 6%, then 24 or 48 hours of EEG monitoring would be worth the cost depending on how much decision makers were willing to pay per quality-adjusted life-year gained. If ESE identification and management improved outcomes by as little as 3%, then 24 hours of EEG monitoring would be worth the cost. CONCLUSIONS EEG monitoring has the potential to be cost-effective if ESE identification and management improves patient outcomes by as little as 3%.
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24
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Kurz JE, Poloyac SM, Abend NS, Fabio A, Bell MJ, Wainwright MS. Variation in Anticonvulsant Selection and Electroencephalographic Monitoring Following Severe Traumatic Brain Injury in Children-Understanding Resource Availability in Sites Participating in a Comparative Effectiveness Study. Pediatr Crit Care Med 2016; 17:649-57. [PMID: 27243415 PMCID: PMC5189641 DOI: 10.1097/pcc.0000000000000765] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
OBJECTIVES Early posttraumatic seizures may contribute to worsened outcomes after traumatic brain injury. Evidence to guide the evaluation and management of early posttraumatic seizures in children is limited. We undertook a survey of current practices of continuous electroencephalographic monitoring, seizure prophylaxis, and the management of early posttraumatic seizures to provide essential information for trial design and the development of posttraumatic seizure management pathways. DESIGN Surveys were sent to site principal investigators at all 43 sites participating in the Approaches and Decisions in Acute Pediatric TBI trial at the time of the survey. Surveys consisted of 12 questions addressing strategies to 1) implement continuous electroencephalographic monitoring, 2) posttraumatic seizure prophylaxis, 3) treat acute posttraumatic seizures, 4) treat status epilepticus and refractory status epilepticus, and 5) monitor antiseizure drug levels. SETTING Institutions comprised a mixture of free-standing children's hospitals and university medical centers across the United States and Europe. SUBJECTS Site principal investigators of the Approaches and Decisions in Acute Pediatric TBI trial. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Continuous electroencephalographic monitoring was available in the PICU in the overwhelming majority of clinical sites (98%); however, the plans to operationalize such monitoring for children varied considerably. A similar majority of sites report that administration of prophylactic antiseizure medications is anticipated in children (93%); yet, a minority reports that a specified protocol for treatment of posttraumatic seizures is in place (43%). Reported medication choices varied substantially between sites, but the majority of sites reported pentobarbital for refractory status epilepticus (81%). The presence of treatment protocols for seizure prophylaxis, early posttraumatic seizures, posttraumatic status epilepticus, and refractory status epilepticus was associated with decreased reported medications (all p < 0.05). CONCLUSIONS This study reports the current management practices for early posttraumatic seizures in select academic centers after pediatric severe traumatic brain injury. The substantial variation in continuous electroencephalographic monitoring implementation, choice of seizure prophylaxis medications, and management of early posttraumatic seizures across institutions was reported, signifying the areas of clinical uncertainty that will help provide focused design of clinical trials. Although sites with treatment protocols reported a decreased number of medications for the scenarios described, completion of the Approaches and Decisions in Acute Pediatric TBI trial will be able to determine if these protocols lead to decreased variability in medication administration in children at the clinical sites.
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Affiliation(s)
- Jonathan E. Kurz
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Samuel M. Poloyac
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA
| | - Nicholas S. Abend
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - Anthony Fabio
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA
| | - Michael J. Bell
- Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Mark S. Wainwright
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL
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25
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How much does it cost to identify a critically ill child experiencing electrographic seizures? J Clin Neurophysiol 2016; 32:257-64. [PMID: 25626776 DOI: 10.1097/wnp.0000000000000170] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVES Electrographic seizures in critically ill children may be identified by continuous EEG monitoring. We evaluated the cost effectiveness of 4 electrographic seizure identification strategies (no EEG monitoring and EEG monitoring for 1 hour, 24 hours, or 48 hours). METHODS We created a decision tree to model the relationships among variables from a societal perspective. To provide input for the model, we estimated variable costs directly related to EEG monitoring from their component parts, and we reviewed the literature to estimate the probabilities of outcomes. We calculated incremental cost-effectiveness ratios to identify the trade-off between cost and effectiveness at different willingness-to-pay values. RESULTS Our analysis found that the preferred strategy was EEG monitoring for 1 hour, 24 hours, and 48 hours if the decision maker was willing to pay <$1,666, $1,666-$22,648, and >$22,648 per critically ill child identified with electrographic seizures, respectively. The 48-hour strategy only identified 4% more children with electrographic seizures at substantially higher cost. Sensitivity analyses found that all 3 strategies were acceptable at lower willingness-to-pay values when children with higher electrographic seizure risk were monitored. CONCLUSIONS The results of this study support monitoring of critically ill children for 24 hours because the cost to identify a critically ill child with electrographic seizures is modest. Further study is needed to predict better which children may benefit from 48 hours of EEG monitoring because the costs are much higher.
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ASET Position Statement on Skin Safety during EEG Procedures - A Guideline to Improving Outcome. Neurodiagn J 2016; 56:296-300. [PMID: 28436802 DOI: 10.1080/21646821.2016.1246336] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
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Dang LT, Shellhaas RA. Diagnostic yield of continuous video electroencephalography for paroxysmal vital sign changes in pediatric patients. Epilepsia 2015; 57:272-8. [PMID: 26660005 DOI: 10.1111/epi.13276] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2015] [Indexed: 11/28/2022]
Abstract
OBJECTIVE We aimed to determine the diagnostic yield of continuous monitoring with video electroencephalography (cVEEG) for pediatric inpatients with paroxysmal vital sign changes (PVSCs), and to identify risk factors for the PVSCs being seizures, based on clinical information available before cVEEG initiation. We hypothesized that PVSCs without nonautonomic symptoms (NAS) were unlikely to be seizures, and also that patients' clinical characteristics would alter the risk of recording seizures. METHODS We performed a single-center chart review of 324 cVEEG studies that were obtained for differential diagnosis of PVSCs. We examined the type of PVSCs that prompted cVEEG, associated NAS, and patient characteristics, and whether the target events or seizures were recorded. We performed logistic regression analyses to determine which patient and semiologic features altered the risk of the PVSCs being seizures, and which patient characteristics altered the risk of recording any seizures. RESULTS Target PVSCs were recorded in 52% (N = 169). Seizures were recorded in 21% (N = 69) of the studies, often unrelated to the PVSCs (N = 39). When examining only PVSCs without NAS, only 4% (3/75) of studies obtained for apnea and 2.1% (1/48) of studies obtained for oxygen desaturation revealed the target events to be seizures. No studies recorded ictal hypertension (0/26), hypotension (0/16), or bradycardia (0/18). In univariate analysis, there was a decreased risk that the events were seizures when PVSCs lacked NAS (odds ratio [OR] 0.23, 95% confidence interval [CI] 0.08-0.65). The risk was increased when the patient had received an antiseizure medication (2.9, 1.3-6.5), the target PVSC was apnea (3.5, 1.5-8.5), and in particular, apnea with NAS (8.7, 3.7-20.8). In adjusted analyses, only apnea with associated NAS independently increased the risk of the PVSCs being seizures (7.7, 3.2-18.5). SIGNIFICANCE PVSCs in the absence of NAS are rarely due to seizures. Ideally, cVEEG should be reserved for children with additional risk factors for seizures, beyond isolated PVSCs.
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Affiliation(s)
- Louis T Dang
- Department of Pediatrics and Communicable Diseases, Division of Pediatric Neurology, C.S. Mott Children's Hospital, University of Michigan, Ann Arbor, Michigan, U.S.A
| | - Renée A Shellhaas
- Department of Pediatrics and Communicable Diseases, Division of Pediatric Neurology, C.S. Mott Children's Hospital, University of Michigan, Ann Arbor, Michigan, U.S.A
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Wilson CA. Continuous electroencephalogram detection of non-convulsive seizures in the pediatric intensive care unit: review of the utility and impact on management and outcomes. Transl Pediatr 2015; 4:283-9. [PMID: 26835390 PMCID: PMC4728999 DOI: 10.3978/j.issn.2224-4336.2015.10.02] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Non-convulsive seizures (NCS) are common among critically ill children with acute encephalopathy. Continuous electroencephalogram (CEEG) monitoring is an indispensable tool to detect NCS, which is essential to guiding management and assessing prognosis. Risk factors for NCS are highest in pediatric intensive care unit (PICU) patients with altered mental status (AMS) and a recently witnessed clinical seizure, acute changes on neuroimaging, and/or interictal abnormalities on CEEG. Screening for at least 24 hours in at risk pediatric populations is ideal, but around half of NCS may be detected within the first hour. Rapid treatment of prolonged seizures or status epilepticus is critical, as higher seizure burdens have been associated with poorer outcomes in critically ill children. This review integrates current information on critically ill children with AMS and the use of CEEGs, risk factors for NCS, duration of CEEG monitoring, and how the detection of NCS impacts management and outcomes.
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Affiliation(s)
- Carey A Wilson
- Department of Child Neurology, University of Utah School of Medicine, UT 84113, USA
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29
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Abend NS, Wagenman KL, Blake TP, Schultheis MT, Radcliffe J, Berg RA, Topjian AA, Dlugos DJ. Electrographic status epilepticus and neurobehavioral outcomes in critically ill children. Epilepsy Behav 2015; 49:238-44. [PMID: 25908325 PMCID: PMC4536172 DOI: 10.1016/j.yebeh.2015.03.013] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Revised: 03/10/2015] [Accepted: 03/11/2015] [Indexed: 01/04/2023]
Abstract
PURPOSE Electrographic seizures (ESs) and electrographic status epilepticus (ESE) are common in children with acute neurologic conditions in pediatric intensive care units (PICUs), and ESE is associated with worse functional and quality-of-life outcomes. As an exploratory study, we aimed to determine if ESE was associated with worse outcomes using more detailed neurobehavioral measures. METHODS Three hundred children with an acute neurologic condition and altered mental status underwent clinically indicated EEG monitoring and were enrolled in a prospective observational study. We obtained follow-up data from subjects who were neurodevelopmentally normal prior to PICU admission. We evaluated for associations between ESE and adaptive behavior (Adaptive Behavior Assessment System-II, ABAS-II), behavioral and emotional problems (Child Behavior Checklist, CBCL), and executive function (Behavior Rating Inventory of Executive Function, BRIEF) using linear regression analyses. A p-value of <0.05 was considered significant. RESULTS One hundred thirty-seven of 300 subjects were neurodevelopmentally normal prior to PICU admission. We obtained follow-up data from 36 subjects for the CBCL, 32 subjects for the ABAS-II, and 20 subjects for the BRIEF. The median duration from admission to follow-up was 2.6 years (IQR: 1.2-3.8). There were no differences in the acute care variables (age, sex, mental status category, intubation status, paralysis status, acute neurologic diagnosis category, seizure category, EEG background category, or short-term outcome) between subjects with and without follow-up data for any of the outcome measures. On univariate analysis, significant differences were not identified for CBCL total problem (ES coefficient: -4.1, p = 0.48; ESE coefficient: 8.9, p = 0.13) or BRIEF global executive function (ES coefficient: 2.1, p = 0.78; ESE coefficient: 14.1, p = 0.06) scores, although there were trends toward worse scores in subjects with ESE. On univariate analysis, ESs were not associated with worse scores (coefficient: -21.5, p = 0.051), while ESE (coefficient: -29.7, p = 0.013) was associated with worse ABAS-II adaptive behavioral global composite scores. On multivariate analysis, when compared to subjects with no seizures, both ESs (coefficient: -28, p=0.014) and ESE (coefficient: -36, p = 0.003) were associated with worse adaptive behavioral global composite scores. DISCUSSION Among previously neurodevelopmentally normal children with acute neurologic disorders, ESs and ESE were associated with worse adaptive behavior and trends toward worse behavioral-emotional and executive function problems. This was a small exploratory study, and the impact of ESs and ESE on these neurobehavioral measures may be clarified by subsequent larger studies. This article is part of a Special Issue entitled "Status Epilepticus".
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Affiliation(s)
- Nicholas S Abend
- Division of Neurology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Katherine L Wagenman
- Division of Neurology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Taylor P Blake
- Psychology Department, Drexel University, Philadelphia, PA, USA
| | | | - Jerilynn Radcliffe
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Robert A Berg
- Department of Anesthesia and Critical Care Medicine, The Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, The Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dennis J Dlugos
- Division of Neurology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Yang A, Arndt DH, Berg RA, Carpenter JL, Chapman KE, Dlugos DJ, Gallentine WB, Giza CC, Goldstein JL, Hahn CD, Lerner JT, Loddenkemper T, Matsumoto JH, Nash KB, Payne ET, Sánchez Fernández I, Shults J, Topjian AA, Williams K, Wusthoff CJ, Abend NS. Development and validation of a seizure prediction model in critically ill children. Seizure 2015; 25:104-11. [PMID: 25458097 PMCID: PMC4315714 DOI: 10.1016/j.seizure.2014.09.013] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Revised: 09/25/2014] [Accepted: 09/29/2014] [Indexed: 11/15/2022] Open
Abstract
PURPOSE Electrographic seizures are common in encephalopathic critically ill children, but identification requires continuous EEG monitoring (CEEG). Development of a seizure prediction model would enable more efficient use of limited CEEG resources. We aimed to develop and validate a seizure prediction model for use among encephalopathic critically ill children. METHOD We developed a seizure prediction model using a retrospectively acquired multi-center database of children with acute encephalopathy without an epilepsy diagnosis, who underwent clinically indicated CEEG. We performed model validation using a separate prospectively acquired single center database. Predictor variables were chosen to be readily available to clinicians prior to the onset of CEEG and included: age, etiology category, clinical seizures prior to CEEG, initial EEG background category, and inter-ictal discharge category. RESULTS The model has fair to good discrimination ability and overall performance. At the optimal cut-off point in the validation dataset, the model has a sensitivity of 59% and a specificity of 81%. Varied cut-off points could be chosen to optimize sensitivity or specificity depending on available CEEG resources. CONCLUSION Despite inherent variability between centers, a model developed using multi-center CEEG data and few readily available variables could guide the use of limited CEEG resources when applied at a single center. Depending on CEEG resources, centers could choose lower cut-off points to maximize identification of all patients with seizures (but with more patients monitored) or higher cut-off points to reduce resource utilization by reducing monitoring of lower risk patients (but with failure to identify some patients with seizures).
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Affiliation(s)
- Amy Yang
- Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at The University of Pennsylvania, United States
| | - Daniel H Arndt
- Departments of Pediatrics and Neurology, Beaumont Children's Hospital and Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States
| | - Robert A Berg
- Department of Anesthesia and Critical Care Medicine, The Children's Hospital of Philadelphia and The Perelman School of Medicine at The University of Pennsylvania, Philadelphia, PA, United States
| | - Jessica L Carpenter
- Department of Neurology, Children's National Medical Center, Washington, DC, United States
| | - Kevin E Chapman
- Department of Pediatrics and Neurology, Children's Hospital Colorado and University of Colorado School of Medicine, Aurora, CO, United States
| | - Dennis J Dlugos
- Departments of Neurology and Pediatrics, The Children's Hospital of Philadelphia and The Perelman School of Medicine at The University of Pennsylvania, Philadelphia, PA, United States
| | - William B Gallentine
- Division of Neurology, Duke Children's Hospital and Duke University School of Medicine, Durham, NC, United States
| | - Christopher C Giza
- Division of Neurology, Department of Pediatrics Mattel Children's Hospital and UCLA Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Joshua L Goldstein
- Division of Neurology, Children's Memorial Hospital and Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Cecil D Hahn
- Division of Neurology, The Hospital for Sick Children and University of Toronto, Toronto, ON, United States
| | - Jason T Lerner
- Division of Neurology, Department of Pediatrics Mattel Children's Hospital and UCLA Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Joyce H Matsumoto
- Division of Neurology, Department of Pediatrics Mattel Children's Hospital and UCLA Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Kendall B Nash
- Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Eric T Payne
- Division of Neurology, The Hospital for Sick Children and University of Toronto, Toronto, ON, United States
| | - Iván Sánchez Fernández
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Justine Shults
- Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at The University of Pennsylvania, United States
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, The Children's Hospital of Philadelphia and The Perelman School of Medicine at The University of Pennsylvania, Philadelphia, PA, United States
| | - Korwyn Williams
- Department of Pediatrics, University of Arizona College of Medicine and Barrow's Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
| | - Courtney J Wusthoff
- Division of Child Neurology, Stanford University, Palo Alto, CA, United States
| | - Nicholas S Abend
- Departments of Neurology and Pediatrics, The Children's Hospital of Philadelphia and The Perelman School of Medicine at The University of Pennsylvania, Philadelphia, PA, United States.
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Abstract
PURPOSE OF REVIEW To discuss the use of continuous video-electroencephalographic (cEEG) monitoring among critically ill children at risk for electrographic seizures and status epilepticus. RECENT FINDINGS Recent reports have demonstrated the growing, but heterogeneous, use of cEEG monitoring among North American pediatric institutions, and provided evidence for the high prevalence of subclinical seizures, particularly among encephalopathic patients with acute brain injury. Increasing seizure burden and status epilepticus have been shown to be independently associated with worse short-term and long-term outcomes. SUMMARY Certain high-risk children frequently experience electrographic seizures and status epilepticus, often without clinical signs, necessitating the use of cEEG monitoring for their diagnosis and management. Although an increasing electrographic seizure burden and status epilepticus are independently associated with worse outcome, further studies are needed to determine whether aggressive use of antiepileptic drugs to reduce seizure burden can improve outcome.
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Abend NS, Wusthoff CJ, Goldberg EM, Dlugos DJ. Electrographic seizures and status epilepticus in critically ill children and neonates with encephalopathy. Lancet Neurol 2014; 12:1170-9. [PMID: 24229615 DOI: 10.1016/s1474-4422(13)70246-1] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Electrographic seizures are seizures that are evident on EEG monitoring. They are common in critically ill children and neonates with acute encephalopathy. Most electrographic seizures have no associated clinical changes, and continuous EEG monitoring is necessary for identification. The effect of electrographic seizures on outcome is the focus of active investigation. Studies have shown that a high burden of electrographic seizures is associated with worsened clinical outcome after adjustment for cause and severity of brain injury, suggesting that a high burden of such seizures might independently contribute to secondary brain injury. Further research is needed to determine whether identification and management of electrographic seizures reduces secondary brain injury and improves outcome in critically ill children and neonates.
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Affiliation(s)
- Nicholas S Abend
- Departments of Neurology and Pediatrics, The Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Wagenman KL, Blake TP, Sanchez SM, Schultheis MT, Radcliffe J, Berg RA, Dlugos DJ, Topjian AA, Abend NS. Electrographic status epilepticus and long-term outcome in critically ill children. Neurology 2014; 82:396-404. [PMID: 24384638 DOI: 10.1212/wnl.0000000000000082] [Citation(s) in RCA: 111] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE Electrographic seizures (ES) and electrographic status epilepticus (ESE) are common in children in the pediatric intensive care unit (PICU) with acute neurologic conditions. We aimed to determine whether ES or ESE was associated with worse long-term outcomes. METHODS Three hundred children with an acute neurologic condition and encephalopathy underwent clinically indicated EEG monitoring and were enrolled in a prospective observational study. We aimed to obtain follow-up data from 137 subjects who were neurodevelopmentally normal before PICU admission. RESULTS Follow-up data were collected for 60 of 137 subjects (44%) at a median of 2.7 years. Subjects with and without follow-up data were similar in clinical characteristics during the PICU admission. Among subjects with follow-up data, ES occurred in 12 subjects (20%) and ESE occurred in 14 subjects (23%). Multivariable analysis indicated that ESE was associated with an increased risk of unfavorable Glasgow Outcome Scale (Extended Pediatric Version) category (odds ratio 6.36, p = 0.01) and lower Pediatric Quality of Life Inventory scores (23 points lower, p = 0.001). Among subjects without prior epilepsy diagnoses ESE was associated with an increased risk of subsequently diagnosed epilepsy (odds ratio 13.3, p = 0.002). ES were not associated with worse outcomes. CONCLUSIONS Among children with acute neurologic disorders who were reported to be neurodevelopmentally normal before PICU admission, ESE but not ES was associated with an increased risk of unfavorable global outcome, lower health-related quality of life scores, and an increased risk of subsequently diagnosed epilepsy even after adjusting for neurologic disorder category, EEG background category, and age.
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Affiliation(s)
- Katherine L Wagenman
- From the Department of Anesthesia and Critical Care Medicine (R.A.B., A.A.T.), The Children's Hospital of Philadelphia and Perelman School of Medicine at the University of Pennsylvania, Philadelphia; Division of Neurology (K.L.W., S.M.S., D.J.D., N.S.A.), The Children's Hospital of Philadelphia; Departments of Neurology and Pediatrics (D.J.D., N.S.A.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia; Psychology Department (T.P.B., M.T.S.), Drexel University, Philadelphia, PA; and Department of Pediatrics (J.R.), Perelman School of Medicine at the University of Pennsylvania, The Children's Hospital of Philadelphia, PA
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A review of long-term EEG monitoring in critically ill children with hypoxic-ischemic encephalopathy, congenital heart disease, ECMO, and stroke. J Clin Neurophysiol 2013; 30:134-42. [PMID: 23545764 DOI: 10.1097/wnp.0b013e3182872af9] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Continuous EEG monitoring is being used with increasing frequency in critically ill children with hypoxic ischemic encephalopathy, congenital heart disease, stroke, and extracorporeal membrane oxygenation (ECMO). The primary indication for EEG monitoring is to identify electrographic seizures and electrographic status epilepticus, which have been associated with worse outcome in some populations. A secondary indication is to provide prognostic information. This review summarizes the available data regarding continuous EEG monitoring in critically ill children with special attention to hypoxic ischemic encephalopathy, congenital heart disease, stroke, and children undergoing ECMO.
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Pediatric ICU EEG monitoring: current resources and practice in the United States and Canada. J Clin Neurophysiol 2013; 30:156-60. [PMID: 23545766 DOI: 10.1097/wnp.0b013e31827eda27] [Citation(s) in RCA: 91] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
PURPOSE To describe current continuous EEG monitoring (cEEG) utilization in critically ill children. METHODS An online survey of pediatric neurologists from 50 US and 11 Canadian institutions was conducted in August 2011. RESULTS Responses were received from 58 of 61 (95%) surveyed institutions. Common cEEG indications are altered mental status after a seizure or status epilepticus (97%), altered mental status of unknown etiology (88%), or altered mental status with an acute primary neurologic condition (88%). The median number of patients undergoing cEEG per month per center increased from August 2010 to August 2011 (6 to 10 per month in the United States; 2 to 3 per month in Canada). Few institutions have clinical pathways addressing cEEG use (31%). Physicians most commonly review cEEG twice per day (37%). There is variability regarding which services can order cEEG, the degree of neurology involvement, technologist availability, and whether technologists perform cEEG screening. CONCLUSIONS Among the surveyed institutions, which included primarily large academic centers, cEEG use in pediatric intensive care units is increasing and is often considered indicated for children with altered mental status at risk for nonconvulsive seizures. However, there remains substantial variability in cEEG access and utilization among institutions.
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Sánchez SM, Arndt DH, Carpenter JL, Chapman KE, Cornett KM, Dlugos DJ, Gallentine WB, Giza CC, Goldstein JL, Hahn CD, Lerner JT, Loddenkemper T, Matsumoto JH, McBain K, Nash KB, Payne E, Sánchez Fernández I, Shults J, Williams K, Yang A, Abend NS. Electroencephalography monitoring in critically ill children: current practice and implications for future study design. Epilepsia 2013; 54:1419-27. [PMID: 23848569 DOI: 10.1111/epi.12261] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/28/2013] [Indexed: 11/29/2022]
Abstract
PURPOSE Survey data indicate that continuous electroencephalography (EEG) (CEEG) monitoring is used with increasing frequency to identify electrographic seizures in critically ill children, but studies of current CEEG practice have not been conducted. We aimed to describe the clinical utilization of CEEG in critically ill children at tertiary care hospitals with a particular focus on variables essential for designing feasible prospective multicenter studies evaluating the impact of electrographic seizures on outcome. METHODS Eleven North American centers retrospectively enrolled 550 consecutive critically ill children who underwent CEEG. We collected data regarding subject characteristics, CEEG indications, and CEEG findings. KEY FINDINGS CEEG indications were encephalopathy with possible seizures in 67% of subjects, event characterization in 38% of subjects, and management of refractory status epilepticus in 11% of subjects. CEEG was initiated outside routine work hours in 47% of subjects. CEEG duration was <12 h in 16%, 12-24 h in 34%, and >24 h in 48%. Substantial variability existed among sites in CEEG indications and neurologic diagnoses, yet within each acute neurologic diagnosis category a similar proportion of subjects at each site had electrographic seizures. Electrographic seizure characteristics including distribution and duration varied across sites and neurologic diagnoses. SIGNIFICANCE These data provide a systematic assessment of recent CEEG use in critically ill children and indicate variability in practice. The results suggest that multicenter studies are feasible if CEEG monitoring pathways can be standardized. However, the data also indicate that electrographic seizure variability must be considered when designing studies that address the impact of electrographic seizures on outcome.
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Affiliation(s)
- Sarah M Sánchez
- Departments of Neurology and Pediatrics, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, Pennsylvania, Philadelphia, USA
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Abstract
Continuous electroencephalographic (CEEG) monitoring is used with increasing frequency in critically ill children to provide insight into brain function and to identify electrographic seizures. CEEG monitoring use often impacts clinical management, most often by identifying electrographic seizures and status epilepticus. Most electrographic seizures have no clinical correlate, and thus would not be identified without CEEG monitoring. There are increasing data showing that electrographic seizures and electrographic status epilepticus are associated with worse outcome. Seizure identification efficiency may be improved by further development of quantitative electroencephalography trends. This review describes the clinical impact of CEEG data, the epidemiology of electrographic seizures and status epilepticus, the impact of electrographic seizures on outcome, the utility of quantitative electroencephalographic trends for seizure identification, and practical considerations regarding CEEG monitoring.
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Abend NS, Arndt DH, Carpenter JL, Chapman KE, Cornett KM, Gallentine WB, Giza CC, Goldstein JL, Hahn CD, Lerner JT, Loddenkemper T, Matsumoto JH, McBain K, Nash KB, Payne E, Sánchez SM, Fernández IS, Shults J, Williams K, Yang A, Dlugos DJ. Electrographic seizures in pediatric ICU patients: cohort study of risk factors and mortality. Neurology 2013; 81:383-91. [PMID: 23794680 DOI: 10.1212/wnl.0b013e31829c5cfe] [Citation(s) in RCA: 143] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVES We aimed to determine the incidence of electrographic seizures in children in the pediatric intensive care unit who underwent EEG monitoring, risk factors for electrographic seizures, and whether electrographic seizures were associated with increased odds of mortality. METHODS Eleven sites in North America retrospectively reviewed a total of 550 consecutive children in pediatric intensive care units who underwent EEG monitoring. We collected data on demographics, diagnoses, clinical seizures, mental status at EEG onset, EEG background, interictal epileptiform discharges, electrographic seizures, intensive care unit length of stay, and in-hospital mortality. RESULTS Electrographic seizures occurred in 162 of 550 subjects (30%), of which 61 subjects (38%) had electrographic status epilepticus. Electrographic seizures were exclusively subclinical in 59 of 162 subjects (36%). A multivariable logistic regression model showed that independent risk factors for electrographic seizures included younger age, clinical seizures prior to EEG monitoring, an abnormal initial EEG background, interictal epileptiform discharges, and a diagnosis of epilepsy. Subjects with electrographic status epilepticus had greater odds of in-hospital death, even after adjusting for EEG background and neurologic diagnosis category. CONCLUSIONS Electrographic seizures are common among children in the pediatric intensive care unit, particularly those with specific risk factors. Electrographic status epilepticus occurs in more than one-third of children with electrographic seizures and is associated with higher in-hospital mortality.
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Affiliation(s)
- Nicholas S Abend
- Department of Neurology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
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